Abstract :
The malicious computer code immune system and the biological immune system are highly similar: both preserve the stability of the system in real time in a constantly changing environment. This similarity is exploited to design a malicious code immune system to solve the malware active defense problem. The malicious code immunization project is mainly composed of four major components: the immune information collection program, immune information filtering processing program, immunization information discrimination program, and immune response program. An imbalanced support vector machine method was applied to optimize output results of malicious code immunization, thereby removing uncertain malicious code immune outputs. This demonstrates in detail the feasibility of the imbalanced support vector machine method in optimizing the immunization program output data. We showed that an imbalanced support vector machines canoptimize the outputs of the malicious code immune system by removing glitches from the outputs. As a result, the machine helps to determine the precise time of the emergence of the immune response.
